The p53 tumor-suppressor protein prevents cancer development through various mechanisms, including the induction of cell-cycle arrest, apoptosis, and the maintenance of genome stability. We have identified a p53-inducible gene named TIGAR (TP53-induced glycolysis and apoptosis regulator). TIGAR expression lowered fructose-2,6-bisphosphate levels in cells, resulting in an inhibition of glycolysis and an overall decrease in intracellular reactive oxygen species (ROS) levels. These functions of TIGAR correlated with an ability to protect cells from ROS-associated apoptosis, and consequently, knockdown of endogenous TIGAR expression sensitized cells to p53-induced death. Expression of TIGAR may therefore modulate the apoptotic response to p53, allowing survival in the face of mild or transient stress signals that may be reversed or repaired. The decrease of intracellular ROS levels in response to TIGAR may also play a role in the ability of p53 to protect from the accumulation of genomic damage.

Germinal center (GC) B cells feature repression of many gene enhancers to establish their characteristic transcriptome. Here we show that conditional deletion of Lsd1 in GCs significantly impaired GC formation, associated with to failure to repress immune synapse genes linked to GC exit, which are also direct targets of the BCL6 transcriptional repressor. We found that BCL6 directly binds and recruits LSD1, primarily to intergenic and intronic enhancers. Conditional deletion of Lsd1 suppressed GC hyperplasia caused by constitutive expression of Bcl6, and significantly delayed Bcl6-driven lymphomagenesis. Administration of LSD1 catalytic inhibitors had little effect on GC formation or GC derived lymphoma cells. Using a CRISPR/Cas9 domain screen we found instead that the LSD1 Tower domain was critical for LSD1 dependency in GC derived B cells. These results indicate an essential role of LSD1 in the humoral immune response, where it modulates enhancer function by forming repression complexes with BCL6.

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.